Why agents make autonomous decisions in Prompt Engineering / GenAI - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define an agent that makes decisions without human input.

Prompt Engineering / GenAI

class Agent:
    def __init__(self):
        self.state = None

    def decide(self, environment):
        return [1]

Drag options to blanks, or click blank then click option'

Aprint('Hello World')

Benvironment.get_best_action()

Cself.state = None

Dreturn None

Attempts:

3 left

💡 Hint

Common Mistakes

Returning None instead of an action.

Using print statements instead of returning an action.

The agent decides by choosing the best action from the environment, enabling autonomous decisions.

2fill in blank

medium

Complete the code to update the agent's state after taking an action.

Prompt Engineering / GenAI

class Agent:
    def __init__(self):
        self.state = None

    def update_state(self, action):
        self.state = [1]

Drag options to blanks, or click blank then click option'

Aself.state

BNone

Cprint(action)

Daction

Attempts:

3 left

💡 Hint

Common Mistakes

Setting state to None instead of the action.

Using print instead of assignment.

The agent updates its internal state to the action it just took, reflecting autonomous decision-making.

3fill in blank

hard

Fix the error in the agent's decision method to return an action string.

Prompt Engineering / GenAI

class Agent:
    def decide(self):
        action = 'move_forward'
        [1] action

Drag options to blanks, or click blank then click option'

Areturn

Bpass

Cyield

Dprint

Attempts:

3 left

💡 Hint

Common Mistakes

Using print instead of return.

Using pass which does nothing.

The decide method must return the action string so the agent can act autonomously.

4fill in blank

hard

Fill both blanks to create a dictionary of actions and their rewards for autonomous decision making.

Prompt Engineering / GenAI

rewards = { 'move': [1], 'stop': [2] }

Drag options to blanks, or click blank then click option'

A10

B0

C-5

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning negative reward to 'move' which discourages action.

Using None instead of numeric rewards.

The 'move' action has a positive reward (10), encouraging the agent to move, while 'stop' has zero reward.

5fill in blank

hard

Fill all three blanks to create a function that selects the best action based on rewards.

Prompt Engineering / GenAI

def best_action(actions, rewards):
    best = None
    max_reward = [1]
    for action in actions:
        if rewards[action] [2] max_reward:
            best = action
            max_reward = rewards[action]
    return [3]

Drag options to blanks, or click blank then click option'

Afloat('inf')

B>

Cbest

Dfloat('-inf')

Attempts:

3 left

💡 Hint

Common Mistakes

Initializing max_reward to positive infinity.

Using '<' instead of '>' in comparison.

Returning None instead of best action.

Initialize max_reward to negative infinity to find max; use '>' to compare; return best action.

Practice

1. Why do autonomous agents make decisions on their own?

easy

A. To always ask for human approval before acting

B. To act quickly and independently without waiting for instructions

C. To avoid learning from their environment

D. To only perform tasks when manually controlled

Solution

1. Step 1: Understand the purpose of autonomy in agents

   Autonomous agents are designed to make decisions without constant human input to save time and act efficiently.

2. Step 2: Connect autonomy to quick and independent action

   Making decisions on their own allows agents to respond faster and handle tasks without delays.

3. Final Answer:

   To act quickly and independently without waiting for instructions -> Option B

4. Quick Check:

   Autonomy means independent action = A [OK]

Hint: Autonomy means acting without waiting for others [OK]

Common Mistakes:

• Thinking agents always need human approval
• Confusing autonomy with manual control
• Believing agents avoid learning from environment

2. Which of the following is the correct way to describe an autonomous agent's decision process?

easy

A. Agent only repeats pre-programmed steps without change

B. Agent waits for user input before every action

C. Agent ignores environment and acts randomly

D. Agent uses environment data to decide actions independently

Solution

1. Step 1: Identify how autonomous agents decide

   Autonomous agents use information from their environment to make decisions without external commands.

2. Step 2: Match description to correct behavior

   Using environment data to decide independently fits the definition of autonomy.

3. Final Answer:

   Agent uses environment data to decide actions independently -> Option D

4. Quick Check:

   Environment data guides decisions = A [OK]

Hint: Autonomous means using environment info to decide [OK]

Common Mistakes:

• Thinking agents always wait for user input
• Believing agents act randomly without reason
• Assuming agents never change behavior

3. Consider this simple agent code snippet:

environment = {'light': 'on'}
agent_state = 'idle'
if environment['light'] == 'on':
    agent_state = 'move'
else:
    agent_state = 'wait'
print(agent_state)

What will the agent print as its state?

medium

A. move

B. error

C. wait

D. idle

Solution

1. Step 1: Check the environment condition

   The environment dictionary has 'light' set to 'on', so the condition environment['light'] == 'on' is true.

2. Step 2: Determine agent state based on condition

   Since the condition is true, agent_state is set to 'move'.

3. Final Answer:

   move -> Option A

4. Quick Check:

   Light on means move = D [OK]

Hint: Check condition true or false to find output [OK]

Common Mistakes:

• Ignoring the environment value and printing 'idle'
• Confusing else branch with if branch
• Expecting a syntax or runtime error

4. This agent code is supposed to decide to 'stop' if obstacle detected, else 'go':

obstacle = true
if obstacle = true:
    action = 'stop'
else:
    action = 'go'
print(action)

What is the error in this code?

medium

A. Using '=' instead of '==' in the if condition

B. Missing colon ':' after the if statement

C. Incorrect indentation of the else block

D. Using 'print' without parentheses

Solution

1. Step 1: Identify the if condition syntax

   The condition uses '=' which is assignment, not comparison. It should be '==' to compare values.

2. Step 2: Confirm correct syntax for if condition

   Using '=' in if causes a syntax error; '==' is needed to check if obstacle is true.

3. Final Answer:

   Using '=' instead of '==' in the if condition -> Option A

4. Quick Check:

   Comparison needs '==' not '=' = B [OK]

Hint: Use '==' to compare, '=' to assign [OK]

Common Mistakes:

• Confusing assignment '=' with comparison '=='
• Forgetting colon after if statement
• Misaligning else block indentation

5. An autonomous cleaning robot uses sensors to detect dirt and obstacles. It must decide to clean, avoid, or recharge. Which approach helps it make the best autonomous decisions?

hard

A. Use fixed rules ignoring sensor data

B. Randomly choose actions without sensing

C. Learn from sensor data and past actions to improve decisions

D. Wait for human commands before every action

Solution

1. Step 1: Understand the role of sensors and learning

   Sensors provide data about the environment; learning helps improve decisions based on experience.

2. Step 2: Identify the best approach for autonomous decision-making

   Learning from sensor data and past actions allows the robot to adapt and make better choices over time.

3. Final Answer:

   Learn from sensor data and past actions to improve decisions -> Option C

4. Quick Check:

   Learning + sensing = better autonomy = C [OK]

Hint: Best autonomy combines sensing and learning [OK]

Common Mistakes:

• Ignoring sensor data and using fixed rules
• Choosing random actions without logic
• Waiting for human commands defeats autonomy